Smart Geo. Guido Satta (Maggio 2015)

SmartGeo/Eiagrid
portal
SmartGeo

Gestore di Risorse Condivise per Analisi di Dati e Applicazioni Ambientali
In-field optimization of seismic data
acquisition by real-time subsurface
imaging
using a remote GRID computing
environment.

Overview
The Grida3 project
The basic concept of EIAGRID
The EIAGRID portal
Data Examples
Conclusions

Aggregating Infrastructure for Environmental Solutions
The great environmental challenges make necessary expertise from
different disciplines and a strong integration of the activities,
based on
Advanced information and communication technologies
New problem solving paradigms
Organizations of federated entities (i.e. peripheral organs) with
common interests and objectives in the management and monitoring
of the environment and the territory must be in condition to set up
shared knowledge-based systems and provide high value-added
activities leading to
Industrial products
Advanced services
tuned to implement environmental quality systems.
The great environmental challenges make necessary expertise from
different disciplines and a strong integration of the activities,
based on
Advanced information and communication technologies
New problem solving paradigms
Organizations of federated entities (i.e. peripheral organs) with
common interests and objectives in the management and monitoring
of the environment and the territory must be in condition to set up
shared knowledge-based systems and provide high value-added
activities leading to
Industrial products
Advanced services
tuned to implement environmental quality systems.

Real Collaborations and Virtual Organizations
Working Group 2:
monitoring, planning and
sustainable water
resource management
Working Group 3: information
systems for the analysis of
integrated environmental and
territorial data
Working Group 1: short
term prediction of
extreme events
A Grid is an
infrastructure that allows
the integrated and
collaborative use of
virtualized resources
 Data servers
 Computational servers
 Connecting networks
 Numerical applications
 Information systems
owned and managed by
one or more organizations
A Grid is an
infrastructure that allows
the integrated and
collaborative use of
virtualized resources
 Data servers
 Computational servers
 Connecting networks
 Numerical applications
 Information systems
owned and managed by
one or more organizations
The virtual organization acts as the Grid
provider while each partner becomes the
recipient of the Grid services
The virtual organization acts as the Grid
provider while each partner becomes the
recipient of the Grid services

Grida3, Gestore di Risorse Condivise per Analisi di Dati e Applicazioni
Ambientali (MIUR Prog. N. 1433/2006)
A problem-solving grid
platform for the integration,
through a computing portal, of
 resources for
 communication
 computation
 data storage
 visualization
 simulation software
 instrumentation
 human know-how
in Environmental Sciences
A problem-solving grid
platform for the integration,
through a computing portal, of
 resources for
 communication
 computation
 data storage
 visualization
 simulation software
 instrumentation
 human know-how
in Environmental Sciences
TECHNOLOGIESTECHNOLOGIES
InfrastructureInfrastructure User InterfacesUser InterfacesSecure accessSecure access
APPLICATIONSAPPLICATIONS
GISGIS
MeteorologyMeteorology
HydrologyHydrology
Site
Remediation
Site
Remediation
Geophysical
Imaging
Geophysical
Imaging

Numerical applications
Grid computing somehow appears as natural extension
of distributed parallel computing
Code porting and new products development are
focused to the domain of advanced SW technology
for the intensive use of geographically distributed
resources
With Grida3 applications, no disruption of algorithmic
or mathematical nature is expected, at least at the
moment
Grid computing somehow appears as natural extension
of distributed parallel computing
Code porting and new products development are
focused to the domain of advanced SW technology
for the intensive use of geographically distributed
resources
With Grida3 applications, no disruption of algorithmic
or mathematical nature is expected, at least at the
moment

Data-dominated applications
Data acquisition costs are tremendously reduced
Environmental sciences are characterized by very
large volumes of heterogeneous data generated by
modern recording digital apparatus
Datasets are not always usable by traditional
prediction solvers (PDEs and ODEs) without a
preliminary conceptualization phase
Use of clever metadata, a describer designed to infer
relationships within data collections and validate
new model hypothesis
Data acquisition costs are tremendously reduced
Environmental sciences are characterized by very
large volumes of heterogeneous data generated by
modern recording digital apparatus
Datasets are not always usable by traditional
prediction solvers (PDEs and ODEs) without a
preliminary conceptualization phase
Use of clever metadata, a describer designed to infer
relationships within data collections and validate
new model hypothesis

Data conceptualization
Need to develop SW tools for empirical analysis in
environmental sciences based on geographical
information systems,
Integrating qualitative and quantitative data
Grouping data by relationships that may not be clearly visible
among collections of field data
Looking for evidence that would support or refute the
implication of a theory
Problems consequently arise at the level of data collection,
retrieval and analysis
Most of the non-trivial knowledge extraction is based on
heuristics operating on distributed databanks:
Data mining discovery - Ontology-based knowledge representations
Need to develop SW tools for empirical analysis in
environmental sciences based on geographical
information systems,
Integrating qualitative and quantitative data
Grouping data by relationships that may not be clearly visible
among collections of field data
Looking for evidence that would support or refute the
implication of a theory
Problems consequently arise at the level of data collection,
retrieval and analysis
Most of the non-trivial knowledge extraction is based on
heuristics operating on distributed databanks:
Data mining discovery - Ontology-based knowledge representations

Groundwater: Modeling, Management and Planning
Groundwater application
 GIS (input&output)
 Pre-processing
 Solver and Optimizer
 Post-processing
 Visualization
Groundwater application
 GIS (input&output)
 Pre-processing
 Solver and Optimizer
 Post-processing
 Visualization
Application
developer
Application
developer
Site 1Site 1
Data grid infrastructure
SRB/iRODS
SRB/iRODS
Compute grid infrastructure
via Genius/EnginFrame
Site 2Site 2
Data integration and problem solving
 WEB collaborative environment
 Customizable analysis tools
 Problem solving driven by physical models
 Web GIS (solver output, field data, maps…)
 Decision Support System (DSS)
Data integration and problem solving
 WEB collaborative environment
 Customizable analysis tools
 Problem solving driven by physical models
 Web GIS (solver output, field data, maps…)
 Decision Support System (DSS)
Environmental
engineer
Environmental
engineer

Groundwater: Modeling, Management and Planning
Site 3Site 3
SRB/iRODS
SRB/iRODS
Environmental
manager
Environmental
manager
Collaborative problem-solving
platform as a decision support
system
 Interactive simulation tools based on
physics
 Web GIS environment for data
 Storage
 Retrieval
 Rendering
 Analysis and decision instruments for
 Management
 Planning
 Costs evaluation
 Editing of results and dissemination
Collaborative problem-solving
platform as a decision support
system
 Interactive simulation tools based on
physics
 Web GIS environment for data
 Storage
 Retrieval
 Rendering
 Analysis and decision instruments for
 Management
 Planning
 Costs evaluation
 Editing of results and dissemination

Grida3, Shared Resources Manager for Environmental Data Analysis
and Applications
The Grida3 portal aims at supporting
problem solving and decision making
by integrating
resources for
communication
computation
data storage
software for
simulation
inversion
visualization
and human know how
into a grid computing platform for
Environmental Sciences
The Grida3 portal aims at supporting
problem solving and decision making
by integrating
resources for
communication
computation
data storage
software for
simulation
inversion
visualization
and human know how
into a grid computing platform for
Environmental Sciences
TECHNOLOGIESTECHNOLOGIES
InfrastructureInfrastructure User InterfacesUser InterfacesSecure accessSecure access
APPLICATIONSAPPLICATIONS
GIS ToolsGIS Tools
MeteorologyMeteorology
HydrologyHydrology
Site
Remediation
Site
Remediation
Geophysical
Imaging
Geophysical
ImagingEIAGRID
Service
EIAGRID
Service

Creating a grid computing environment for in-field QC and
Optimization of SR/GPR data acquisition by:
1. Providing a web-browser-based user
interface easily accessible from the field
1. On-the-fly processing of the seismic field
data using a remote GRID environment
1. Fast optimization of data analysis and
imaging parameters by parallel
processing of alternative workflows
The EIAGRID Portal
Main Objectives

Creating a data grid environment to facilitate analysis &
decision making in integrated multi-disciplinary studies by:
1. Providing a flexible and
customizable data grid management
architecture
using iRODS
1. Georeferencing the data using Geo
Information System (GIS)
technologies
1. Interconnecting the different types of
data by mesh-generators and data
crossing techniques
The EIAGRID Portal
Main Objectives

Seismic reflection data acquisition

GPR data
Multi-offset GPR data:
Aim: monitoring of water content and water
conductivity
Target depth: 0 - 5 m
2D line: length 55 m
RAMAC/GPR CU II with MC4 +
4 unshielded 200 Hz antennas
Number of sources: 546
Source spacing: 0.1m
Number of receivers: 28
Receiver spacing: 0.2 m
Maximum offset: 0.6 m

SR/GPR data: Fields of application
Environmental engineering:
 Detection of problematic solid-waste in dumping grounds
 Control of the topography of the impermeable basement
 Characterization of landslides on slopes proximal to the
ground rupture
Seismic and geotechnical engineering:
 Evaluation of the seismic local response
Hydrogeology:
 Identification of aquifer boundaries
 Estimation of hydrological parameters (porosity, fluid
content, etc.)

Near-surface geophysics
• Near-surface geophysics is the use of geophysical
methods to investigate small-scale features in the
shallow (tens of meters) subsurface.
• It is closely related to exploration geophysics.
• Methods used include seismic refraction and reflection,
gravity, magnetic, electric, and electromagnetic
methods.
• These methods are used for archaeology,
environmental science, forensic science, military
intelligence, geotechnical investigation, and
hydrogeology.
• near-surface geophysics includes the study of
biogeochemical cycles.

Hydrogeophysics
• Hydrogeophysics involves use of geophysical
measurements for estimating parameters and
monitoring processes that are important to
hydrological studies: water resources,
contaminant transport, ecological and climate
investigations.
• Improved characterization and monitoring using
hydrogeophysical techniques can lead to
improved management of our natural resources,
understanding of natural systems, and
remediation of contaminants.

Main Steps of SR/GPR Data Processing
PREPROCESSING
GEOMETRICAL
PROCESSING
WAVELET
PROCESSING
IMAGING
Principalelaborationsteps
Data conversion
Geometry setup
Trace editing
Noise atenuationCMP sorting
Velocity analysis and NMO
Residual static
CMP stacking
Deconvolution
Seismic migration
Seismic Records Processing Phases Subsurface Image
Input System Output

Seismic reflection data processing
Seismic Records
Input System Output
Processing Phases Subsurface Image

Conventional velocity analysisFinal Processing Results

Main Problem of SR/GPR acquisition:Main Problem of SR/GPR acquisition:
Real-time processing is difficult and cost intensive
 Acquisition parameters such as recording time,
sampling interval, source strength and receiver
spacing cannot be optimized in the field
Solution:
Wireless data
transmission + remote
GRID computing facilities

Main Concept

25
EIAGRID
Portal
1. Web-browser-based interface accessible from the field
2. Real-time processing using distant HPC resources
3. Remote collaboration and acquisition controlling
Features

26
EIAGRID
Portal
1. Project, data and user management
2. Simplistic toolbox for data visualization and manipulation
3. Data-driven imaging method suited for parallel computing
Components

•Riduzione dei tempi di Elaborazione
•Ottimizzazione delle fasi di acquisizione
Obietti
vi:
Abbattere i costi e trasformare la
Sismica a Riflessione in una
tecnica di indagine appetibile e di
Routine in campo ambientale e
ingegneristico

• CRS-Stack per la Sismica
Superficiale
• Analisi di velocità completamente
automatica
• Trasferimento dei dati sismici, via
cellulare, dal sito al centro di calcolo;
• Elaborazione in tempo reale;
• trasferimento sezione sismica dal
centro di calcolo al sito per
ottimizzazione
acquisizione
Implementazioni:

Remote Grid Computing
Preprocessing and Visualization using SU:
 Basic preprocessing steps can be applied without installing
the complex SU processing package.
 Data-driven CRS imaging technology---state-of-the-art in oil
exploration---enables highly automated data processing.
Imaging and RSC using CRS technology:
 GRID deployment using high performance computing
facilities provides the necessary computing power.
Parallel processing of different Processing
workflows: Cumbersome sequential optimization of processing
workflow and processing parameters speeds up drastically.

ZERO-OFFSET AND CMP METHODS
• The simplest type of acquisition
would be to use a single coincident
source and receiver pair and profile
the earth along a line
• Such an experiment would be called
a zero-offset experiment because
there is no offset distance between
source and receiver
• The resulting seismic data will be
single-fold because there will only
be a single trace per sub-surface
position.
• The zero-offset concept is an
important one and the method might
be used in practise if noise could be
ignored.

• In order to overcome the noise
problem and to estimate earth
velocity, the method of acquisition
most commonly used is the
Common-Mid-Point (CMP) method.
• The general idea is to acquire a
series of traces (gather) which
reflect from the same common
subsurface mid-point.
• The traces are then summed
(stacked) so that superior signal-to-
noise ratio to that of the single-fold
stack results.
• The fold of the stack is determined
by the number of traces in the CMP
gather.

• Traces resulting from a single six-fold CMP
gather depicting reflections from a single flat
interface
• The reflection from the flat interface produces
a curved series of arrivals on the seismic
traces since it takes longer to travel to the far
offsets than the near offsets.
• This hyperbolic curve (red line) is called the
Normal Moveout curve or NMO and is related
to travel time, offset and velocity
• Before stacking the NMO curve must be
corrected such that the seismic event lines up
on the gather. Normal Moveout Correction.
• The results are shown in the central portion of
the figure. The moveout corrected traces are
then stacked, to produce the 6-fold stack
trace, which simulates the zero-offset
response but with increased signal-to-noise
ratio.
• The CMP gather provides information about
seismic velocity of propagation

CMP in practice
• CMP acquisition is accomplished by firing the
source into many receivers simultaneously
• (a) a shot gather where a single shot (red) is
fired into six receivers (green). A receiver is
also co-located with the shot to produce a
zero-offset trace. By moving the source
position an appropriate multiple of the
receiver spacing CMP gathers can be
constructed by re-ordering the shot traces
(this process is called sorting).
• (b) shows the original shot and second shot
(traces in red). In this case, the shot has
moved up a distance equal to the receiver
spacing. The CMP spacing is equal to half
the receiver spacing.
• (c) shows how the fold of the CMP gathers is
starting to build up after six shots have been
fired. At the beginning of the line the fold
builds up to it's maximum of three. The fold
stays at the maximum until the end of the line
is reached where the fold decreases.

EFFECT OF DIPPING HORIZONS
• The CMP method holds for multiple layers and the data can be moved
out and stacked to produce three reflections.
• Where dip is present it is clear that the CMP method is breaking down
since the traces do not all reflect from the same mid-point location.
Processing techniques such as DMO and Migration are required to
accurately process CMP data acquired from dipping strata.

Common-Reflection-Surface stack
• (generalized) stacking velocity analysis
– search for stacking operator fitting best actual reflection event
– based on coherence analysis
• data-driven stacking with CRP trajectories
– fitting a space curve to a traveltime surface
highly ambiguous, hardly applicable
• solution:
• consider entire reflector segments
– i. e., consider neighboring CRPs
– i. e., consider local curvature of reflector
– fitting spatial operator to traveltime surface
– three stacking parameters

Basic idea
Observations:
– conventional stack implicitly relies on reflector continuity
(this also applies to NMO + DMO correction)
– based on normal rays for offset zero
– we have band-limited data
• Fresnel zone concept
Consequences:
If conventional stack works
– there are neighboring reflection points
– they physically contribute to the wavefield at a considered CMP
Why shouldn’t we incorporate these
neighboring reflection points?

CRS stack
• Features inherited from conventional stack:
– normal ray concept
– assumption of reflector continuity
– analytical traveltime approximation (2nd order)
– coherence analysis yields stacking parameters
– stack yields simulated zero-offset section
• Additional features:
– incorporates neighboring CMP gathers
– yields additional stacking parameters
– increases the coverage
– improves reflector continuity and S/N ratio

From CMP to CRS stacking:
Figure taken from Perroud and Tygel 2005. NMO velocity analysis for the CMP at position x = 10 m.
45
Conventional CMP-by-CMP velocity analysis:

CRS-STACK 3D
Lo stacking consente di comprimere i dati, con aumento S/N,
simulando una acquisizione zero-offset
(source-receiver)
L’idea del CRS STACK e’ descrivere la propagazione
d’onda mediante una geometria locale (ottica parassiale):
raggi + fronti d’onda paraboloide-ellittico

Massimizzazione di una funzione peso: Semblance
( )hHKHhmHKHmmw T
zyNIPzy
TT
zyNzy
TT
++





+=
0
0
2
0
0
2 22
v
t
v
tthyp
2 parameters ( emergence angle & azimuth )
3 Normal Wavefront parameters ( KN,11; KN,12 ; KN22 )
3 NIP Wavefront parameters ( KNIP,11; KNIP,12 ; KNIP22 )
Definizione del problema
• Problema di ottimizzazione:
– Ricerca di 8 parametri per il fit di un’ipersuperficie in
uno spazio pentadimensinale
( )
( )
( )
( )
∑ ∑
∑ ∑
+
−=′ =
′
+
−=′ =
′






=
2
2
2
2
1
2
,
2
1
,
N
i
N
ii
i
N
i
N
ii
i
t
tt
M
i
ti
t
tt
M
i
ti
f
f
SC
SC = SCmax
Legame fra Semblance e parametri sono i dati sismici

Ricerca velocità NMO
Metodi data-driven: trovare la velocità con algoritmi di ottimizzazione di
funzioni di coerenza come la semblance
Basta limitare lo spazio di ricerca: due possibilità
2
2
22
20
nmo
hyp
v
h
tt += ( )
( )
( )
( )
∑ ∑
∑ ∑
+
−=′ =
′
+
−=′ =
′






=
2
2
2
2
1
2
,
2
1
,
N
i
N
ii
i
N
i
N
ii
i
t
tt
M
i
ti
t
tt
M
i
ti
f
f
SC
Limiti assoluti uguali per ogni punto della
linea d’acquisizione
time min max
0.00 1500.0 1900.0
0.10 1540.0 2200.0
0.20 1550.0 2450.0
0.30 1575.0 2800.0
0.50 1600.0 3300.0
0.70 1600.0 3800.0
Limiti relativi ad una mappa di velocità data
con un valori diversi per ogni punto
time min max
0.00 -30.0 +10.0
0.10 -45.0 +25.0
0.20 -45.0 +30.0
0.30 -10.0 +50.0
0.50 -20.0 +85.0
0.70 -80.0 +100.0
• Possibiltà di ricerche ricorsive

Strategia adottata
Step III
Determination of
RNIP parameters
From VNMO,min ,
VNMO,max and γv (Step I)
and ψ,θ (Step II),
determination of
RNIP,min, RNIP,max
and γNIP
3D ZO Stack
one five-parametric
search for ψ, θ, γN,
RN,min and RN,max
Step II
Automatic 3D CMP
Stack
one three-parametric
search for VNMO,min ,
VNMO,max and γv.
Step I
3D ZO CRS Stack
Stack using the eight
paramenters within the
projectet Fresnel Zones
using the complete CRS
operator
Step IV
Separare le ricerche dei
parametri utilizzando
sottodomini dei dati
Soluzione possibile grazie
alla formulazione di ipotesi
plausibili
( )hHKHhmHKHmmw T
zyNIPzy
TT
zyNzy
TT
++





+=
0
0
2
0
0
2 22
v
t
v
tthyp
Possibilità di ripetere più volte
ogni singolo step

Data-driven stacking parameter determination
Each stacking parameter triple within a given search range
defines a hypothetical second-order reflection response.
The optimum parameter triple maximizes the coherence
between this prediction and the actually measured data.

53
Stacking parameter search:
Pragmatic search:
3 x 1 parameter line search in specific
gathers (Mann et al. 1999)
+ Cloud = Real-time imaging
One step search:
1 x 3 parameter surface search in
prestack data (Garabito et al. 2001)
+ Cloud = High-precision imaging
Figs: Mann et al. 2007

CRS stacking result obtained after 4 minutes using 50 CPU

Time domain imagingTime domain imaging
Published in: Perroud, H., and Tygel, M., 2005, Velocity estimation by
the common-reflection-surface (CRS) method: Using ground-
penetrating radar: Geophysics, 70, 1343–1352.
Results GPR data

Conclusions
SMARTGEO
 ...minimizes the software and hardware requirements
needed to perform a successful SR/GPR data acquisition.
 ...reduces the complexity of data QC and choice of
acquisition parameter for less experienced users.
 …provides fast and accurate results by using modern
imaging technology and high performance computing.
Enables a wider use of SR/GPR surveys in environmental
and earth sciences through Grid technologies
 … facilitates the creation of an integrated geophysical
database for environmental studies.

Smart Geo. Guido Satta (Maggio 2015)

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